Packages
Case studies
Load data
For one lemma
Comparative analyses
Usage intensity
uses <- get_uses(tweets)
uses_tot <- get_uses_tot(uses)
age = get_age(uses)
coef_var <- get_coef_var(uses)
mean_date <- get_mean_date(uses)
max_date <- get_max_date(uses)
uses_month <- conv_uses_month(uses)
uses_plt <- plt_uses(uses_month, lemma, mean_date, max_date)
ggplotly(uses_plt)
`geom_smooth()` using method = 'loess' and formula 'y ~ x'
Advanced (S-curve)
Candidates
- ghosting
- deep learning
- artificial intelligence
- co-working
- climate emergency
Degree centralization
Diachronic
df_comp %>%
filter(LEMMA ==lemma) %>%
select(SUBSET, CENT_DEGREE) %>%
mutate(SUBSET = factor(SUBSET, levels=c('first', 'mean', 'max', 'last', 'full'))) %>%
ggplot(., aes(x=SUBSET, y=CENT_DEGREE, group=1)) +
geom_point() +
geom_line()

Comparative analyses
Processing status
Lemma list
df_comp %>%
select(LEMMA, SUBSET, STAMP) %>%
filter(SUBSET == 'full') %>%
mutate(STAMP = as_datetime(STAMP)) %>%
arrange(desc(STAMP))
Dataset statistics
df_comp %>%
filter(SUBSET == 'full') %>%
select(LEMMA, SUBSET, USES, USERS) %>%
dplyr::summarise(
USES_TOT = sum(USES),
USERS_TOT = sum(USERS)
)
Degree centrality
Overall
List
df_comp %>%
select(LEMMA, SUBSET, USES, CENT_DEGREE) %>%
filter(
SUBSET == 'full',
USES >= 10000
) %>%
arrange((CENT_DEGREE))
Plot
plt <- df_comp %>%
select(LEMMA, SUBSET, USES, CENT_DEGREE) %>%
filter(SUBSET == 'full') %>%
arrange((CENT_DEGREE)) %>%
ggplot(., aes(x=CENT_DEGREE, y=reorder(LEMMA, CENT_DEGREE))) +
geom_point() +
scale_x_continuous(trans='log')
ggplotly(plt)
Over time
Across all lemmas
df_comp %>%
filter(
SUBSET != 'full',
# USES > 2000
EDGES >= 100
) %>%
group_by(SUBSET) %>%
summarize(CENT_AVG = mean(CENT_DEGREE)) %>%
mutate(SUBSET = factor(SUBSET, levels=c('first', 'mean', 'max', 'last'))) %>%
ggplot(., aes(x=SUBSET, y=CENT_AVG, group=1)) +
geom_point() +
geom_line()

Biggest changes
df_comp %>%
select(LEMMA, SUBSET, CENT_DEGREE, EDGES) %>%
filter(
SUBSET %in% c('first', 'last'),
EDGES >= 100
) %>%
dplyr::group_by(LEMMA) %>%
dplyr::mutate(CENT_DIFF = lag(CENT_DEGREE) - CENT_DEGREE) %>%
drop_na() %>%
select(-SUBSET) %>%
rename(CENT_LAST = CENT_DEGREE) %>%
arrange(desc(CENT_DIFF))
Usage intensity vs. network characteristics
Uses vs. degree centralization
Plot
plt <- df_comp %>%
filter(
SUBSET == 'full',
# USES >= 1000
) %>%
select(LEMMA, CENT_DEGREE, USES, EDGES) %>%
ggplot(., aes(x=CENT_DEGREE, y=USES)) +
geom_text(aes(label=LEMMA)) +
scale_y_continuous(trans='log') +
scale_x_continuous(trans='log') +
geom_smooth(method=lm)
ggplotly(plt)
Correlation
df_corr_full <- df_comp %>%
filter(
SUBSET != 'full',
EDGES >= 100
) %>%
select(-c(LEMMA, SUBSET, NET_WINDOW_DATES, SKIP, STAMP, NROWS))
cor.test(df_corr_full$USES, df_corr_full$CENT_DEGREE)
Pearson's product-moment correlation
data: df_corr_full$USES and df_corr_full$CENT_DEGREE
t = -2.0737, df = 337, p-value = 0.03887
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.216180217 -0.005795898
sample estimates:
cor
-0.1122458
Degree centrality vs. communities
Correlation
df_comp %>%
filter(SUBSET == 'last') %>%
select(CENT_DEGREE, COMMUNITIES) %>%
mutate(COMMUNITIES = as.numeric(COMMUNITIES)) %>%
correlate()
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
Plot
df_comp %>%
filter(SUBSET == 'last') %>%
select(LEMMA, CENT_DEGREE, COMMUNITIES) %>%
ggplot(., aes(x=CENT_DEGREE, y=as.numeric(COMMUNITIES))) +
geom_text(aes(label=LEMMA)) +
scale_x_continuous(trans='log')

Uses vs. users
Plot
plt <- df_comp %>%
filter(SUBSET == 'full') %>%
select(LEMMA, USES, USERS) %>%
ggplot(., aes(x=USERS, y=USES)) +
geom_text(aes(label=LEMMA)) +
scale_x_continuous(trans='log') +
scale_y_continuous(trans='log') +
geom_smooth(method=lm)
ggplotly(plt)
Correlation
df_comp %>%
filter(SUBSET == 'full') %>%
select(USES, USERS) %>%
correlate()
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
Coefficient of variation
df_comp %>%
filter(
SUBSET == 'full',
USES >= 1000
) %>%
select(LEMMA, USES, COEF_VAR) %>%
arrange(desc(COEF_VAR))
COEF_VAR vs. CENT
df_comp %>%
filter(SUBSET == 'full') %>%
select(LEMMA, COEF_VAR, CENT_DEGREE) %>%
ggplot(., aes(y=COEF_VAR, x=CENT_DEGREE)) +
geom_text(aes(label=LEMMA)) +
scale_y_continuous(trans='log')

# geom_smooth(method=lm)
Correlations: EDA
library(Hmisc)
df_corr <- df_comp %>%
# filter(SUBSET == 'last') %>%
select(-c(LEMMA, SUBSET, NET_WINDOW_DATES, SKIP, STAMP, NROWS))
# select(-c(USERS, AGE)) %>%
# mutate(FOCUS = USES) %>%
# focus(FOCUS) %>%
# ggplot(., aes(reorder(rowname, FOCUS), FOCUS)) +
# geom_col() +
# coord_flip()
# rearrange() %>%
# shave() %>%
# rplot()
# network_plot(min_cor=.5) %>%
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